File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation

TitleA Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation
Authors
KeywordsThoracic diseases detection and segmentation
SAR-Net
ChestX-Det
Issue Date2021
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at https://ieee-tmi.org/
Citation
IEEE Transactions on Medical Imaging, 2021, v. 40 n. 8, p. 2042-2052 How to Cite?
AbstractInstance level detection and segmentation of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images. Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN. The SAR-Net consists of three relation modules: 1. the anatomical structure relation module encoding spatial relations between diseases and anatomical parts. 2. the contextual relation module aggregating clues based on query-key pair of disease RoI and lung fields. 3. the disease relation module propagating co-occurrence and causal relations into disease proposals. Towards making a practical system, we also provide ChestX-Det, a chest X-Ray dataset with instance-level annotations (boxes and masks). ChestX-Det is a subset of the public dataset NIH ChestX-ray14. It contains ~3500 images of 13 common disease categories labeled by three board-certified radiologists. We evaluate our SAR-Net on it and another dataset DR-Private. Experimental results show that it can enhance the strong baseline of Mask R-CNN with significant improvements. The ChestX-Det is released at https://github.com/Deepwise-AILab/ChestX-Det-Dataset.
Persistent Identifierhttp://hdl.handle.net/10722/302418
ISSN
2023 Impact Factor: 8.9
2023 SCImago Journal Rankings: 3.703
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLian, J-
dc.contributor.authorLiu, J-
dc.contributor.authorZhang, S-
dc.contributor.authorGao, K-
dc.contributor.authorLiu, X-
dc.contributor.authorZhang, D-
dc.contributor.authorYu, Y-
dc.date.accessioned2021-09-06T03:32:00Z-
dc.date.available2021-09-06T03:32:00Z-
dc.date.issued2021-
dc.identifier.citationIEEE Transactions on Medical Imaging, 2021, v. 40 n. 8, p. 2042-2052-
dc.identifier.issn0278-0062-
dc.identifier.urihttp://hdl.handle.net/10722/302418-
dc.description.abstractInstance level detection and segmentation of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images. Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN. The SAR-Net consists of three relation modules: 1. the anatomical structure relation module encoding spatial relations between diseases and anatomical parts. 2. the contextual relation module aggregating clues based on query-key pair of disease RoI and lung fields. 3. the disease relation module propagating co-occurrence and causal relations into disease proposals. Towards making a practical system, we also provide ChestX-Det, a chest X-Ray dataset with instance-level annotations (boxes and masks). ChestX-Det is a subset of the public dataset NIH ChestX-ray14. It contains ~3500 images of 13 common disease categories labeled by three board-certified radiologists. We evaluate our SAR-Net on it and another dataset DR-Private. Experimental results show that it can enhance the strong baseline of Mask R-CNN with significant improvements. The ChestX-Det is released at https://github.com/Deepwise-AILab/ChestX-Det-Dataset.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at https://ieee-tmi.org/-
dc.relation.ispartofIEEE Transactions on Medical Imaging-
dc.rightsIEEE Transactions on Medical Imaging. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectThoracic diseases detection and segmentation-
dc.subjectSAR-Net-
dc.subjectChestX-Det-
dc.titleA Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation-
dc.typeArticle-
dc.identifier.emailYu, Y: yzyu@cs.hku.hk-
dc.identifier.authorityYu, Y=rp01415-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TMI.2021.3070847-
dc.identifier.pmid33819152-
dc.identifier.scopuseid_2-s2.0-85103884827-
dc.identifier.hkuros324829-
dc.identifier.volume40-
dc.identifier.issue8-
dc.identifier.spage2042-
dc.identifier.epage2052-
dc.identifier.isiWOS:000679532100009-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats